Unsupervised color image segmentation with color-alone feature using region growing pulse coupled neural network

Abstract

Abstract Unsupervised color image segmentation based on low level color features aims to assign same label to all pixels of a region with color homogeneity, which underlies many higher level processing such as object detection and recognition. Pulse coupled neural network (PCNN) is a kind of biologically inspired spiking neural network, and has an inherent image segmentation nature which can combine each pixel's intensity and its spatial relationship with neighboring pixels well. But PCNN cannot deal with color images, which restricts its applications greatly. For the problem, this article studied the color information embedding into PCNN and presented an unsupervised color image segmentation algorithm based on the proposed color region growing PCNN (CRG-PCNN) model. First, RGB color space is converted into Lab in which color distance can be evaluated linearly. Then a linking control unit (LCN) is introduced which essentially is a switch triggered by assessing the color distance among PCNN neuron feeding inputs. When the color distance between two pixels is less than a predefined threshold, a linking between the corresponding neurons is established. By doing so, color information can be embedded into PCNN effectively. Next, the L channel of an input image is fed into CRG-PCNN which can automatically pick out a seed neuron in each iteration and facilitates the region growing continuously by modifying the linking coefficient linearly with the assistance of a fast linking mechanism until certain termination conditions are met. Four widely used quantitative indices for massive experiments conducted on Berkeley segmentation dataset verify the performance of the proposed method. It has good segmentation accuracy and is concise and intuitive.